TEST Data Mapping

Giving meaning to data

Written by Andres Perez-Manriquez
Updated over a week ago

The TEST application has a powerful and flexible data model specifically designed to store experimental data, thus it is important for the user to understand how to exploit it. Every data table loaded in the application has a particular set of columns that describe the information recorded in an experiment, and TEST implements a set of column types (a.k.a metadata) that give meaning to such data schemas. More on the available column types in this glossary.

The user can define its own metadata that best suits its experiments, either through CRUD operations using the Teselagen API (TEST API > Metadata) or directly through the browser application, by accessing to the Meta data configuration page as shown below.

This page enables custom definition of all the different supported metadata or column types used to give meaning to experimental data.

Some of the most relevant column types supported by TEST are the following:

  • Assay Subject Class: Classes of "subjects" under evaluation (e.g.: clones, strains, lines, bioreactors, etc.). They are a way of categorizing subjects (experimental units) into classes for organization or evaluation.

  • Descriptor Type: Describes a particular subject (e.g.: growth conditions for a particular strain, volume of a bioreactor, etc.). These are usually intrinsic features or characteristics of a particular subject. These provide useful contextual or additional information on the subjects being measured.

  • Measurement Target: A measurable object (e.g.: a particular metabolite).

  • Measurement Type: A specific type of measurement performed on a Measurement Target (e.g.: the concentration of a particular metabolite).

  • Reference Dimension: These are different dimensions along which different measurement observations are carried out (e.g.: time, distance, etc.)

  • Unit: Linked to Measurement Types and Reference Dimensions and correspond to the unit of a numeric value (e.g.: ug/mL, mM, hours, etc.).

  • Unit Scale: Unit Scales need to be defined for "Units". This allows the conversion to different unit scales, for example from the Imperial System of Units to the International System of Units

  • Unit Dimension: Unit Scales and Measurement Types require Unit Dimensions. It gives information about what type of physical measurement is being carried out. Examples of Unit Dimensions can be: "concentration", "volume", "density", "pressure", "mass", etc.

TEST Meta data customization enables the representation of wide variety of experiments, thus understanding how to configure and define column types is extremely important.

The following diagram visually explains how some of the column types described above are related.

Experimental data in TEST is organized into assays (i.e., experimental runs) which follow the above diagram. For a particular assay, TEST identifies each "subject under evaluation" as an Assay Subject. Each subject under evaluation can be described with different Descriptor Types that characterizes it (i.e., environmental temperature or different cell growth conditions). Also multiple Measurement Types can be associated to it as observations or measures carried out through a particular Reference Dimension (concentration of metabolite through time).

How the user customizes its Meta data, will strictly depend on what type of experimental data is being worked with. The flexibility provided by the TEST application enables the understanding of the most common sorts of data such as: strain optimization, metabolomics, proteomics, transcriptomics, bioprocess optimization, and others.

Data Mapping Example

Following the strain optimization example dataset of the Test Data Importer article, the following image shows a visual mapping between the data and TEST Meta data.

In this experiment, the concentration of a particular metabolite was measured for different strain designs. The TEST Data Importer, asks the user to give meaning to each of the five columns in the data table:

  • Strain ID: Maps to an Assay Subject Class. Each strain ID (1 through 5) corresponds to a different subject under evaluation.

  • Enzyme A: Maps to a Descriptor Type, it characterizes the strain subject with a type of enzyme it produces.

  • Enzyme B: Maps to a Descriptor Type, it characterizes the strain subject with a type of enzyme it produces.

  • Production: Maps to a Measurement Type. Corresponds to the end concentration of a particular target metabolite obtained with the strain.

  • Production Unit: Maps to the unit used to interpret the numeric values of each measurement.

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